The present disclosure relates to methods and systems for processing images.
It is well known that any pair of images, taken from two different positions, contains parallax information relating to the range to various objects in the scene. A three dimensional point cloud of features/objects can be constructed from stereo ranging measurements to the various scene features. However, if the physical locations of the cameras are unknown, the physical size of the point cloud will remain unknown and the cloud is defined as unscaled. In order to properly scale the point cloud to its true physical dimensions, the true camera locations should be known as well as the focal length (or angle calibration) of the camera.
If the camera is moved to three undefined locations, along some trajectory, it is possible to generate at least two stereo pairs of images. Each of these image pairs can generate a three dimensional point cloud of scene features. However, rescaling, rotating, and merging point clouds generated from multiple unknown camera positions is a challenging task. The problem compounds as the camera is moved to multiple positions so as to create multiple stereo pairs of images.
Existing techniques for merging point clouds from multiple images do not provide satisfactory results. The merged point clouds obtained with existing techniques only roughly approximate the original structures being modeled. Furthermore, these techniques require that all essential parts of a scene be visible to all the imagery.